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<p>The process of scientific inquiry is critical for students to understand how knowledge is developed and validated. Representations of the process of inquiry have varied over time, from simple to complex, but some concepts are persistent – such as the concept of a scientific hypothesis. Current guidelines for undergraduate biology education prioritize developing student competence in generating and evaluating hypotheses but fail to define the concept and role of hypotheses. The nature of science literature points to the hypothetico-deductive method of inquiry originated by Karl Popper as a widely accepted conception of scientific hypotheses. Popper characterized a hypothesis as a falsifiable explanation of observed phenomena deduced from previously established knowledge. Alongside hypotheses, Popper also emphasizes the role of predictions, which are logically derived from hypotheses and characterized as testable expectations regarding the outcomes of an experiment or study. Together, hypotheses and predictions are thought to provide a framework for establishing rigorous conclusions in scientific studies. However, the absence of explicit definitions of hypotheses, or predictions, in guidelines and assessment for biology higher education makes it difficult to determine the current relevance of this perspective on hypotheses and predictions in teaching and learning. This leaves us with an unanswered question – what do biology undergraduate students need to know about scientific hypotheses? We addressed this question over three studies each investigating conceptions of scientific hypotheses, and the related concept of predictions, in a different context – (a) contemporary biology research communications, (b) a case study of biology faculty, graduate teaching assistants, and undergraduate students at a single institution, and (c) a national survey of biology faculty members. We found that the terms “hypothesis” and “prediction” used in varied ways in biology research communication and, most notably, often not connected with each other. We also found variation in conceptions of both hypothesis and prediction among faculty members, both in our case study and in the national survey. Our results indicate that faculty members did not always distinguish between the terms hypothesis and prediction in research or teaching or approach them the same way in research contexts. However, they had largely consistent ideas of the underlying reasoning connecting these concepts to each other and to scientific inquiry. Among graduate teaching assistants and undergraduate students in the case study, we found variation in conceptions of both hypotheses and predictions that was different from conceptions held by faculty members. Both graduate teaching assistants and undergraduate students often did not connect the two concepts in terms of underlying reasoning. Overall, our results indicate that there are some misalignments between students’ and instructors’ conceptions of hypotheses and predictions and their role in inquiry. We further discuss these findings in the context of teaching implications for undergraduate biology.</p>

<p dir="ltr">In 2011, the American Association for the Advancement of Science (AAAS) published a report titled “Vision & Change: A Call to Action” that called for reform in undergraduate biology education. The report proposed core competencies that educators should target so students are graduating ready to tackle 21st-century challenges. Of these core competencies is the ability to reason quantitatively, which includes graphing. However, undergraduate biology students struggle with applying essential graph knowledge. The following dissertation project addresses these challenges by exploring two graphing tasks: constructing versus evaluating graphs. We primarily focused on introductory biology students' reasoning practices in applying graph knowledge between these two tasks. As such, we used a digital performance-based assessment tool, <i>GraphSmarts</i>, to analyze students' graphing choices and their justifications in an ecology-based scenario. Chapter 2 discusses the findings of these analyses (n=301), which revealed a disconnect in graph knowledge application between students' graph construction and evaluation skills. While students tend to create basic bar graphs when constructing graphs, they prefer more sophisticated representations, such as bar graphs with averages and error bars, during evaluation tasks—suggesting that the framing of a task influences students' application of graph knowledge between their recognition of effective data representation and their ability to produce such graphs independently. While insightful, we needed to explore ‘why’ this variation exists. Chapter 3 explores the root of this variation through student interviews (n=12). Students would complete the two tasks, followed by questions that help clarify their thought processes. Through the lens of the Conceptual Dynamics framework and the Dynamic Mental Construct model, the study identified two critical cognitive patterns, ‘mode-switching’ and ‘mode-stability.’ Results reaffirm the context-dependent nature of students' graphing knowledge and the influence of task framing on their reasoning processes, as seen in Chapter 2. Results from this project can inform recommendations that biology educators can consider, including 1) having students conduct multiple types of graphing tasks beyond construction, 2) teaching statistical features more explicitly by integrating them into course content, and 3) encouraging students to reflect on their graphing practices. That would be expected to address these instructional needs and foster characteristics of quantitative reasoning and graphing that transfer out of biology. Future directions on this work include exploring other standard graphing tools (Excel, R studio) on graph knowledge, examining the transferability of graphing skills across biological sub-disciplines, and developing targeted interventions for gaps in students' graphing competencies across various graphing tasks. Overall, the work contributes toward developing evidence-based instructional strategies that will be supportive in cultivating competent, robust quantitative reasoning and graphing skills among undergraduate biology students.</p>

In der Dissertation wird der Frage nachgegangen, welche kognitiven Prozesse der Informationsverarbeitung von wissenschaftlichen Daten beim konzeptuellen Lernen von biologischen Inhalten eine Rolle spielen. Dafür wird ein theoriebasiertes Modell des data-based scientific reasoning beschrieben, welches ebenfalls Aspekte der visuellen Wahrnehmung einbezieht. Dabei kann eine Synthese sogenannter bottom-up- und top-down-Effekte die visuelle Wahrnehmung der dargestellten Daten beeinflussen. Von Interesse sind hierbei Charakteristika der Repräsentation der Daten, sowie die individuellen Vorstellungen zum Kontext und ob, die Daten diese stützen oder widerlegen (anomale Daten). Weiterhin wird in der Arbeit die Rolle von data-based scientific reasoning auf wissenschaftliche Modellierungsprozesse untersucht, da Modellieren als übergreifende wissenschaftliche Praxis gesehen werden kann. In vier empirischen Studien wurden kognitive Prozesse beim Umgang mit Daten bei angehenden Biologielehrkräften, mit Fragebögen, Eye Tracking und lautem Denken untersucht. Die Ergebnisse der durchgeführten Studien heben die Rolle der visuellen Wahrnehmung als entscheidenden Schritt beim data-based scientific reasoning hervor. In Bezug auf Charakteristika der Repräsentation erscheint es notwendig, die visuelle Aufmerksamkeit der Lernenden auf spezifische Merkmale zu lenken und mit entsprechendem konzeptuellem Wissen zu verknüpfen. Die Ergebnisse legen nahe, dass anomale Daten zwar mit wissenschaftlich adäquatem Wissen erklärt werden, dies aber nicht automatisch zur Änderung ursprünglicher Vorstellungen bezüglich des Kontexts führt. Diese Tendenz scheint mit Unsicherheit gegenüber den Daten verbunden zu sein. Darüber hinaus spielen anomale Daten eine zentrale Rolle in wissenschaftlichen Modellierungsprozessen. Die Ergebnisse der entsprechenden Studie deuten darauf hin, dass hoch ausgeprägte Kompetenzen des data-based scientific reasoning mit elaborierten Modellierungsprozessen zusammenhängen. / The thesis focusses on the relevance of reasoning with scientific data for conceptual learning in biology. It aims to contribute to the question which cognitive processes are involved when learners encounter data sets that support or contradict their individual expectations. Therefore, a theoretical model of data-based scientific reasoning is described that is grounded on a general model of information processing. In this model, aspects of visual perception are emphasized. However, visual perception is influenced by a synthesis of so-called bottom-up and top-down effects. Furthermore, the theoretical perspective on the role of responses to anomalous data for scientific practices was extended by investigating how learners react and integrate anomalous data during modeling processes. In four empirical studies cognitive processes leading to responses to anomalous data in science education were investigated by applying a mixture of questionnaires, eye tracking techniques, and think aloud. The findings of the conducted studies highlight the role of perceptual processes as a key step during data-based scientific reasoning. Regarding representational characteristics it seems necessary to guide learners´ visual attention and connect specific features with corresponding conceptual knowledge. However, the findings suggest that a use of adequate scientific knowledge for explaining anomalous data does not lead to a change in initial conceptions regarding the context, since participants of the studies still tended to maintain their initial expectations. This tendency seems to be linked to a high need for encompassing information and a connected uncertainty towards the perceived data. Furthermore, anomalous data play a central role during modelling processes. The results of the corresponding study, indicate that sophisticated competencies of data-based reasoning with anomalous data relate to elaborate modeling processes.

<p>External representations (ERs) used in science education are multimodal ensembles consisting of design elements to convey educational meanings to the audience. As an example of a dynamic ER, an animation presenting its content features (i.e., scientific concepts) via varying the feature’s depiction over time. A production team invited the dissertation author to inspect their creation of a biochemistry animation about the role of the actin cytoskeleton in cell motility and the animation’s implication on learning. To address this, the author developed a four-step methodology entitled the Multimodal Variation Analysis of Dynamic External Representations (MVADER) that deconstructs the animation’s content and design to inspect how each content feature is conveyed via the animation’s design elements.</p><p><br></p><p> </p><p>This dissertation research investigated the actin animation’s educational value and the MVADER’s utility in animation evaluation. The research design was guided by descriptive case study methodology and an integrated framework consisting of the variation theory, multimodal analysis, and visual analytics. As stated above, the animation was analyzed using MVADER. The development of the actin animation and the content features the production team members intended to convey via the animation were studied by analyzing the communication records between the members, observing the team meetings, and interviewing the members individually. Furthermore, students’ learning experiences from watching the animation were examined via semi-structured interviews coupled with post- storyboarding. Moreover, the instructions of MVADER and its applications in studying the actin animation were reviewed to determine the MVADER’s usefulness as an animation evaluation tool.</p><p><br></p><p> </p><p>Findings of this research indicate that the three educators in the production team intended the actin animation to convey forty-three content features to the undergraduate biology students. At least 50% of the student who participated in this thesis learned thirty-five of these forty-three (> 80%) features. Evidence suggests that the animation’s effectiveness to convey its features was associated with the features’ depiction time, the number of identified design elements applied to depict the features, and the features’ variation of depiction over time.</p><p><br></p><p>Additionally, one-third of the student participants made similar mistakes regarding two content features after watching the actin animation: the F-actin elongation and the F-actin crosslink structure in lamellipodia. The analysis reveals the animation’s potential design flaws that might have contributed to these common misconceptions. Furthermore, two disruptors to the creation process and the educational value of the actin animation were identified: the vagueness of the learning goals and the designer’s placement of the animation’s beauty over its reach to the learning goals. The vagueness of the learning goals hampered the narration scripting process. On the other hand, the designer’s prioritization of the animation’s aesthetic led to the inclusion of a “beauty shot” in the animation that caused students’ confusion.</p><p><br></p><p> </p><p>MVADER was used to examine the content, design, and their relationships in the actin animation at multiple aspects and granularities. The result of MVADER was compared with the students’ learning outcomes from watching the animation to identify the characteristics of content’s depiction that were constructive and disruptive to learning. These findings led to several practical recommendations to teach using the actin animation and create educational ERs.</p><p><br></p><p> </p><p>To conclude, this dissertation discloses the connections between the creation process, the content and design, and the educational implication of a biochemistry animation. It also introduces MVADER as a novel ER analysis tool to the education research and visualization communities. MVADER can be applied in various formats of static and dynamic ERs and beyond the disciplines of biology and chemistry.</p>